An driverless vehicle means sensing a surrounding environment of a vehicle through various sensors, and controlling a steering direction and a speed of the vehicle according to information about roads, vehicle positions and obstacles obtained by sensing, so that the vehicle can safely and reliably travel on the road.
A laser radar is an important sensor for the driverless vehicle to sense a 3-dimensional environment. After scanning around a scenario, the laser radar returns a point cloud of the 3-dimensional space of the scenario, namely, 3D point cloud, including 3-dimensional coordinates of each point, laser reflection intensity and the like.
It is possible to, based on the collected 3D point cloud, perform detection of the obstacle and feed it back to a planning control system to perform an obstacle-avoiding operation. It can be seen that obstacle detection is directly related to the travel safety of the driverless vehicle and is of very important significance. The obstacles may include pedestrians, motor vehicles, bicycles and the like appearing on the road.
In the prior art, detection of obstacles is performed mainly in the following manner: first, collecting many 3D point clouds and marking all obstacle samples, then using these samples to learn/train a model, and when an actual detection needs to be performed, inputting the collected 3D point clouds into the model to obtain a detection result of obstacles.
However, the above manner has certain problems in practical application: for example, in an early period, obstacle samples need to be marked from a lot of 3D point clouds to train the model, which is time-consuming and laborious and increases the workload. Furthermore, in actual use, there might be confronted with an obstacle that is never met upon training so that the obstacle cannot be detected, i.e., missed detection of the obstacle might occur, thereby reducing the accuracy of the detection result.